Use of behavioral portraits in web site analysis

ABSTRACT

A method is provided for determining a website user behavioral portrait based on navigation on the website and dynamically reconfiguring web pages based on those portraits. In accordance with the method, data relating to the progress of a user through a website is recorded, and an ongoing behavioral portrait of the user is built based on the data. The portrait is then used to dynamically reconfigure web content.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to methods for customizing webpage content, and more specifically, to methods for generating userbehavioral portraits based on web site navigation and search behavior,and for dynamically reconfiguring web page content based on suchportraits to produce personalized web page content.

BACKGROUND OF THE DISCLOSURE

As e-commerce has evolved into a widespread means of doing business,online competition among merchants has increased dramatically. Much ofthe attention in online marketing has been directed towards placingadvertisements for products or services as close to a spending decisionas possible, since this is often a significant factor in an onlinemerchant's likelihood of success.

As a specific example, a car rental company might design their websiteso that it is likely to turn up as a relevant hit when a consumer uses asearch engine to search the term “car rental”. The company might evenpurchase prioritization from one or more businesses that manage popularsearch engines such as YAHOO!® or GOOGLE® so that their web site willappear near the top of the search results page whenever terms indicatingan interest in car rentals are input into the search engine. In somecases, the company may even go the additional step of purchasing bannerads or pop-ups that are triggered by relevant search queries.

While the foregoing approach may be part of a sound online marketingstrategy, it suffers from the drawback that it relies upon an overtmanifestation of consumer interest to identify potential purchasers of aproduct or service. Consequently, such an approach may miss asignificant number of sales opportunities, simply because it identifiesmany potential purchasers of a product or service well after a spendingdecision has been made. In the interim, the consumer may have beenexposed to a wide variety of competing products and services.

Some of the more recent refinements in online marketing have focused onplacing products or services even closer to a spending decision bylooking for more subtle clues to a consumer's interests. Referring backto the previous example, the car rental company may place advertisementson web sites that help consumers to purchase airline tickets, based onthe realization that a significant number of people who are purchasingairline tickets will also require a rental car. However, while this typeof approach may also form part of a sound online strategy, it is foundedon correlations that may be weak. Hence, this type of approach oftenyields a low success rate.

Other methods of online marketing have evolved which seek to matchadvertising content to perspective purchasers based on relevancedetermined from broad demographic information or consumer purchasehistory. For example, some websites use pop-up ads and banners whosecontent is selected based on the gender and age of a consumer providedduring web site registration, on information gleaned from previouson-line purchases by the consumer, or on the geographic regionindicated, for example, by the user's IP address.

However, methods which rely on data obtained from web site registrationare of limited utility, since many consumers are hesitant to spend timeon websites completing forms and profiles for what is perceived to be oflittle benefit. Methods based on broad demographic informationfrequently have a low success rate, since they are necessarily based onbroad generalizations which may not apply to a given consumer. Methodsbased on purchase history are prone to error, since simple productrelationships based on previous purchases can be misleading. Previouspurchases may have no bearing on the consumer's personal interests, asmay be the case if those purchases represent gifts purchased for others.Moreover, even if the previous purchases were for the consumer'spersonal enjoyment, those purchases may not represent the consumer'scurrent interests. For example, the fact that a consumer's browsinghistory or previous purchases indicate a past interest in travel doesnot mean that the consumer has a current interest in travel. Theconsumer may have exhausted all of his vacation time, and is nowinterested in goods and services commensurate with a regular workschedule.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following figures, like reference numerals indicate likeelements.

FIG. A1 is a flowchart illustrating some of the general methodologiesdescribed herein.

FIG. A2 is an illustration of a network over which behavioral portraitsmay be gathered in accordance with some of the methodologies describedherein.

FIG. A3 is an illustration of user actions which may be analyzed in thebuilding of a behavioral portrait in accordance with some of themethodologies described herein.

FIG. A4 is an illustration of some of the questions which can beanswered with the information provided by some of the systems andmethodologies described herein, as compared to the information gleanedby conventional methodologies.

FIG. B1 is an illustration of a particular, non-limiting embodiment of anetwork equipped with a dedicated server appliance for implementing someof the software and methodologies taught herein.

FIG. B2 is an illustration of a particular, non-limiting embodiment of anetwork equipped with a dedicated server appliance for implementing someof the software and methodologies taught herein.

FIG. B3 is an illustration of a particular, non-limiting embodiment of anetwork equipped with a dedicated server appliance for implementing someof the software and methodologies taught herein.

FIG. B4 is an illustration of a particular, non-limiting embodiment of anetwork architecture for implementing some of the software andmethodologies taught herein.

FIG. C1 is an illustration of a web page.

FIG. C2 is an illustration of the web page of FIG. C1 with a coloroverlay illustrating the categorization of objects appearing thereon.

FIG. D1 is a chart depicting the various levels of webpage customizationpossible in accordance with some embodiments of the methodologiesdescribed herein.

FIG. D2 is an illustration of a standard web page as compared to aportrait enhanced web page of the type which may be generated throughthe application of some of the methodologies described herein.

FIG. D3 is an illustration depicting the web pages of FIG. D2 in greaterdetail.

FIG. D4 is an illustration of some of the behavioral characteristics ofa particular portrait type.

FIG. D5 is an illustration of some of the behavioral characteristics ofa particular portrait type.

FIG. D6 is an illustration of a behavioral portrait corresponding to theportrait type depicted in FIG. D4.

FIG. D7 is an illustration of a behavioral portrait corresponding to theportrait type depicted in FIG. D5.

FIGS. D8 through D13 illustrate modifications to a web page in light ofthe portrait depicted in FIG. D6.

FIGS. D14 through D19 illustrate modifications to a web page in light ofthe portrait depicted in FIG. D7.

FIG. E1 is a flowchart illustrating some of the methodologies describedherein which utilize output analytics derived from user behavioralportraits.

FIG. E2 is an illustration of some of the output analytics which may beobtained from the methodology depicted in FIG. D1.

FIG. E3 is an illustration of some of the questions a website owner oronline marketer may obtain answers to with the output analytics obtainedfrom the methodology depicted in FIG. E1.

FIG. E4 is a graph showing the percentage of visits to a web site as afunction of user behavioral portrait.

FIG. E5 is a graph showing the distribution of visits to a website as afunction of user behavioral portrait.

FIG. E6 is a graph showing the percentage of return visits to a web siteas a function of user behavioral portrait.

FIG. E7 is a graph showing the distribution of return visits to awebsite as a function of user behavioral portrait.

FIG. E8 is a graph showing the percentage of closures or abandonment ofa web page as a function of user behavioral portrait.

FIG. E9 is a graph showing the distribution of closures or abandonmentof a web page as a function of user behavioral portrait.

FIG. E10 is a graph showing the distribution of the average amount oftime spent on a web site as a function of user behavioral portrait.

FIG. E11 is a graph showing the distribution of the average number oflinks selected on a web site as a function of user behavioral portrait.

FIG. F1 is an illustration of a particular, non-limiting embodiment of acustomer passport which may be obtained in accordance with some of themethodologies described herein.

FIG. F2 is a flowchart illustrating some of the methodologies describedherein which involve generation of a customer passport.

SUMMARY OF THE DISCLOSURE

In one aspect, a method for dynamically reconfiguring web pages based onuser behavioral portraits is provided which comprises (a) recording datarelating to the behavior of a user on a website; (b) building abehavioral portrait of the user based on the data; and (c) dynamicallyreconfiguring web content based on the behavioral portrait.

In another aspect, a method for developing user behavioral portraits isprovided which comprises (a) recording data relating to the behavior ofa user on a website; and (b) building a behavioral portrait of the userbased on the data.

In another aspect, a method for reconfiguring web pages is providedwhich comprises (a) providing a behavioral portrait of a user; and (b)dynamically reconfiguring web content based on the behavioral portrait.

In a further aspect, a method for providing customized web pages isprovided which comprises (a) receiving a request for a web page from aclient associated with a user; (b) modifying the requested web page inlight of a behavioral portrait developed for the user; and (c) providingthe modified web page to the user in place of the requested web page.

In still another aspect, a system for providing customized web pages isprovided which comprises (a) a first server adapted to provide web pagesto a client associated with a user and being further adapted to receiveinput from the user when the user accesses features on the web pages;(b) a software program adapted to (i) receive user input from the firstserver, (ii) create a behavioral portrait of the user based on the userinput, and (iii) dynamically update the behavioral portrait asadditional user input becomes available, thereby creating an updatedbehavioral portrait; and (c) a second server adapted to alter thecontent of the web pages displayed by said first server based on theupdated behavioral portrait.

In yet another aspect, a system for providing customized web pages isprovided which comprises (a) a server in communication with a clientover a network; and (b) a software program adapted to develop abehavioral portrait of a user associated with the client based oncaptured data relating to the online behavior of the user.

In still a further aspect, a method for doing business is provided whichcomprises (a) providing a computing device equipped with a softwareprogram adapted to (i) receive user input from a server, (ii) create abehavioral portrait of the user based on the user input, and (iii)utilize the behavioral portrait to modify a web page; (b) utilizing thecomputing device to produce modified web content; (c) providing themodified web content to a web site provider; and (d) charging the website provider a fee based on the modified web content displayed on theweb site.

In another aspect, a method for doing business is provided whichcomprises (a) providing a behavioral portrait of a user to a third partyentity, the behavioral portrait being based on user input gatheredduring an online session that the user was involved in; and (b)assessing a first fee to the third party based on the third party'sacceptance of the portrait.

In still another aspect, a method for doing business is provided whichcomprises (a) creating a behavioral portrait of a user based on userinput gathered during an online session that the user was involved in;(b) using the behavioral portrait to determine a recommended manner ofinteracting with the user; and (c) selling to another party a documentcontaining the recommended manner of interacting with the user.

In yet another aspect, a method of analyzing a website is provided whichcomprises (a) recording data relating to the online behavior of aplurality of users through the website; (b) building a behavioralportrait for each of the plurality of users based on the data; (c)categorizing the behavioral portraits of the plurality of users into aplurality of portrait types; and (d) analyzing the behavior of theplurality of users on the website as a function of portrait type.

In still another aspect, a method for analyzing a web page is providedwhich comprises (a) categorizing the features appearing on the web pagein terms of at least one behavioral trait which selection of the featurewould indicate; and (b) creating a graphical overlay which reflects thecategorization of the features.

DETAILED DESCRIPTION A. Overview

One major shortcoming of the above described marketing methods,especially when they are applied in online marketing contexts, is thatthey focus almost exclusively on identifying potential purchasers of aproduct or service, rather than focusing on the manner in which theproduct or service is being presented. Consequently, these approachesfail to apply psychological principles to the identification andaccommodation of a consumer's preferred purchase patterns. In theequivalent human-to-human interaction attendant to a sales opportunity,body language, interaction, dialog and physical indicators may allinfluence the tone, form and content of any given conversation. Thesecues are critical to the success of any face-to-face sales meeting, anda skilled salesperson will utilize these cues to quickly adapt hisapproach as necessary to maximize the likelihood of success.

By contrast, such psychological principles have not been appliedheretofore to equivalent web-based dialogs. In particular, methodscurrently utilized for implementing online advertising and selecting webcontent fail to ascertain the reason and motivation a given onlineconsumer has for closing a sale. These methods ignore the manner inwhich a given consumer prefers to make decisions, and fail to tailor thepresentation of information to a consumer's preferences (e.g., inaccordance with how the consumer prefers to have information presentedto them).

Continuing the car rental example given above and applying it tovisitors to the website of a car rental company, a first visitor to thewebsite may be looking for a vehicle that offers excitement andentertainment for a camping trip. A second visitor may be looking for animage of success and style in a vehicle. A third visitor may be lookingfor the safest vehicle. In addition, each of these visitors may havetheir own unique approaches to making a spending decision. For example,one visitor may prefer to make a spending decision based largely on howothers rate a particular product offering, while another visitor mayprefer to make a spending decision after reviewing a detailed comparisonof product performance specifications. A website that fails to promptlyrecognize each of these diverse motivations, and to present appropriatecontent in an appropriate manner that is suitably prioritized to reflectthe user's preferred approach to making a spending decision, may strikeone or more of these users as being not particularly relevant to thatuser's interests. As a result, the user may navigate to a different,possibly competing web site.

It has now been found that the above noted needs may be met throughsystems, methods and software of the type described herein which analyzean individual's online behavior so as to derive the individual'sbehavioral portrait, and which use that behavioral portrait to modifythe manner in which information is presented to the individual. In thecontext of a buying situation, a behavioral portrait is thepsychological profile of an individual as it pertains to theindividual's preferences in that situation, including their preferreddecision-making approach, their motivation for making the purchase, andthe manner in which they prefer to have information presented to them.

FIG. A1 provides an overview of some of these systems and methodologies.As seen therein, the starting point for many of these systems andmethodologies is the capture a1-1 of the web behavior of one or moreindividuals. This behavior is then analyzed and is used to develop abehavioral portrait a1-2 of the individual. The resulting behavioralportrait may then be put to a variety of end uses.

In some embodiments, the methodologies described herein, and the systemsand software which implement them, apply scientific and psychologicalprinciples to improve the way that information is presented to a websiteuser a1-5. These methodologies may be used to emphasize informationwhich is particularly relevant to a user's current needs and state ofmind, and to de-emphasize information which, although possiblyimportant, may be ancillary to a particular user's interests or decisionmaking process. Consequently, these methodologies may be utilized tocustomize websites so that they appear to have been defined for a givenuser's particular needs at a particular time. As a result, navigationaround the website will seem more natural for the user, transactionclosure procedures will be appropriately tailored so that they are moreappropriate for the situation and the user's current state of mind, andmarketing offers on the site will be customized for user behavior asbefits the time and context.

Preferably, the behavioral portrait is utilized to dynamically (andpossibly automatically) customize, reconfigure or personalize web pages,web content and/or web sites so that the resulting web pages are moreconducive to an individual's state of mind. As a result, theindividual's experience in navigating a website may be more rewarding.In an e-commerce setting, this may result in a greater number of salesclosures, and may have the effect of improving website applicability,sales, and return rates, while also providing specific valuableinformation which may be utilized to differentiate a given website fromcompetitive offerings. Hence, in some embodiments, the methodologiesdescribed herein may be utilized to provide dynamic and personalized webcontent which is adapted to customer buying behaviors.

In other applications, an individual's behavioral portrait (orinformation pertaining thereto) may be provided to human salespeople,call centers, marketing teams and the like a1-3 for use in determininghow to interact with that individual. These parties may use thebehavioral portrait (or information pertaining thereto) to betterunderstand how to communicate with the individual, how to handleobjections the individual may have, and how to close a sale with theindividual.

In still other applications, analyses may be performed on the manner inwhich individuals interact with a website or web page as a function oftheir behavioral portrait a1-4. These analyses may be utilized, forexample, to refine a website or web page to make it more attractive to atarget set of users, to help a business better understand its customerbase and how to interact with that customer base, and to identifyproblem areas with a website or web page. In particular, these analysesmay be utilized to help the owner of a website or a web marketingprogram to determine how to improve e-commerce closure rates, tounderstand how to improve visitor return rates, to understand whycustomers are leaving the website, to increase the effectiveness of webmarketing programs and offline programs, and to understand how best tocommunicate with various customer segments.

As seen in FIG. A2, the capture of web behavior may be accomplishedthrough the use of software running on servers a2-3 which remotely andtransparently monitor the online behavior of users a2-1 on a web site.The pages on the website (or the templates from which these pages arederived) may be provided with appropriate JavaScript or other suitableweb applications which categorize and tag every relevant action that auser can take. These actions may include the pages a user navigates, theitems the user clicks on, search terms entered by the user (includingthe entry of narrowing searches), check out information, pages abandonedby the user, selection of “top 10” items by the user, and other suchinformation. FIG. A3 depicts various actions a visitor may take on asingle web page and various paths the visitor may take through a website. These actions, and the paths taken to get to a given action, maybe used to build a psychological behavioral portrait for that visitor.

FIG. A4 summarizes some of the information the foregoing methodologyprovides and contrasts it to the information provided by conventionalmethods (such as polls) utilized to gather information about consumers.As seen therein, the information a4-7 gathered by conventionaltechniques typically includes relevance information (information onpricing, promotions, purchase history, products, or typical “crowd”behavior), demographics (including information such as age, location,gender or income), or analytical information (information such as auser's operating system, the number of closures on a website, the numberof visitors to the website, the number of browsers who have abandonedthe website, and the browser types being utilized to browse the website). While such information may be useful, it provides very littleinformation about the consumer's personality or state of mind. Hence,while this information may be used to identify potential customers, itprovides very little useful information about how and when to interactwith the customer.

By contrast, methodologies are possible in accordance with the teachingsherein which provide a variety of information very specific to thecustomer and the customer's current state of mind. This information isvery useful in understanding how, and when, to interact with thecustomer. This includes information on the customer's decision-makingprocess a4-1 (how the customer makes purchase decisions), information onthe customer's motivations a4-3 (what motivates that customer to closeon a transaction), information on how the customer prefers to haveinformation presented to them a4-5, an indication of when in the productlifecycle the customer is most likely to make a purchase a4-2, themarketing messages and images that are likely to appeal to the customera4-4, and the level of detail appropriate for the customer a4-6.

The methodologies described herein are especially useful in the contextof e-commerce, and hence, frequent reference will be made to theapplication of these methodologies within this context. However, it willbe appreciated that many of the methodologies described herein arebroadly applicable to the customization of web content in any context tomake it more compatible with the user's behavioral portrait. Thus,unless otherwise indicated, the methodologies and systems describedherein should not be construed as being specifically limited to theiruse in e-commerce settings.

B. Hardware and Network Implementation

FIGS. B1 and B2 depict one particular, non-limiting embodiment of asystem which may be used to implement some of the software andmethodologies disclosed herein. This system essentially consists of afront end and a back end. At the front end of the system, thenavigational attributes of a user are captured, and those attributes areutilized to develop a behavioral portrait for the user. At the back endof the system, the behavioral portrait so generated is utilized todynamically reconfigure web content.

With reference to FIG. B1, the network b1-1 in this particularembodiment has a server side b1-3 and a client side b1-5. The serverside b1-3 comprises a SAN/NAS (Storage Area Network/Network AttachedStorage) storage farm b1-7 comprising a plurality of storage devicesb1-9, a database server b1-11, a variety of application servers b1-13, adedicated server appliance b1-15 which runs the software forimplementing the methodology described herein, a server farm b1-17 whichincludes a plurality of web servers b1-19, and a firewall b1-21.

The web servers b1-19 communicate with a plurality of client devicesb1-23 and with a plurality of business partners b1-25 through thefirewall b1-21 and over a suitable WAN (wide area network) b1-27 such asthe Internet. The business partners b1-23 will typically be businessesthat wish to market goods or services over the WAN b1-27. In some cases,these parties may supply advertising content to the web servers b1-19.

FIG. B2 illustrates how the network b1-1 depicted in FIG. B1 may beutilized to generate user portraits and to dynamically reconfigure webcontent based on the user portraits. As seen therein, each web serverb1-19 receives behavioral information from a user of the WAN b1-27. Thatbehavioral information is typically in the form of context sensitivemouse clicks, keyboard entries, searches, menu selections, verbalcommands (in the case, for example, of devices utilizing voicerecognition software), and other types of user input.

The dedicated server appliance b1-15 is equipped with an applicationprogramming interface (API) b1-41 which enables it to receive andanalyze user input from the server b1-19. The API b1-41 further includesa portrait database b1-43 which stores existing user portraits, and aportrait generator b1-45 which generates new user portraits based on theuser input. In the event that the user already has a portrait stored inthe portrait database b1-43, the portrait generator b1-45 recognizesthis fact and modifies the existing user portrait as new informationabout the user's behavior becomes available. The API b1-41 then forwardsthe resulting user portrait to an application server b1-13. Theapplication server b1-13 acts upon the user portrait by generatingre-mashed web content which is personalized to the user's behavioralportrait, and then passes the re-mashed web content to the web serverb1-19 for transmission to the appropriate client device b1-23.

In a preferred mode of operation, the dedicated server appliance b1-15continuously updates the user portrait database b1-43 in real time asnew input from a user becomes available, and promptly passes an updatedportrait (or the updated portion thereof) for the user to theapplication server b1-13. The application server b1-13, in turn, servesup re-mashed content to the user. Hence, the dedicated server applianceb1-15 provides dynamic reconfiguration of web page content, andpersonalizes subsequent web pages for that user.

FIG. B3 depicts a second embodiment of a system made in accordance withthe teachings herein. The system b3-1 depicted therein is similar inmany respects to the system depicted in FIGS. B1-B2. However, while thesystem depicted in FIGS. B1-B2 passes a portrait between the serverappliance b1-15 and the application server b1-13 to generate re-mashedweb content, in the system b3-1 depicted in FIG. 3, the server applianceb3-15 generates the re-mashed web content directly. Thus, in this systemb3-1, the server appliance b3-15 receives and analyzes user input fromthe server b3-19, builds a behavioral portrait for the user (or modifiesan existing behavioral portrait for the user), generates a new web page(or Portrait Enhanced Page (PEP)), and provides the PEP to thee-commerce web server b3-19 for display to the user (assuming that thee-commerce site accepts the PEP).

It will thus be appreciated that the server appliance b3-15 in FIG. 3may be implemented as a “black box” device which intercepts userbehavioral data and outputs re-mashed web pages based on that data. Sucha device is advantageous in some applications in that it provides ameans by which the provider of the device can extract a revenue streamfrom the re-mashed web content using various business models, some ofwhich are described in greater detail below.

The manner in which the server appliance b3-15 achieves the foregoingfunctionalities may be appreciated from FIG. B3. As seen therein, theserver appliance b3-15 comprises an API b3-41 that receives user inputfrom the server b3-19 and outputs PEPs to the server b3-19, and whichcommunicates with the application server b3-13 as necessary toaccomplish these tasks. The server appliance b3-15 further includes acustomer portrait engine b3-45 which assembles and modifies customerportraits based on behavioral data captured from the server b3-19, apage interpreter b3-47 which interprets the various objects present on aweb page, a PEP generator b3-49 which utilizes the customer portrait togenerate PEPs, and a PEP database b3-51 where the PEPs so generated arestored. Though not explicitly shown, the server appliance b3-15 mayfurther include a portrait database for storing customer portraitsgenerated by the customer portrait engine b3-45.

The server appliance b3-15 in the system b3-01 of FIG. B3 is furtheradapted to communicate customer portraits, or information contained inor relating to customer portraits, to various third parties b3-55. Suchthird parties b3-55 may include, without limitation, sales people,marketing teams, and call centers, and may utilize this data to moreeffectively communicate with the customer, either directly or via theInternet or another suitable network.

The particular, non-limiting embodiments depicted in FIGS. B1-B3implement some of the methodologies described herein by incorporation ofa dedicated server appliance into the server side of the network. Suchan appliance solution is useful in some applications in that it providesthe ability to architect the solution “out-of-band” with the currentarchitecture, and also provides a platform for the owners of a websiteto develop specific content for a user's portrait in the future (thismay include, for example, specific marketing offers on an e-commercewebsite). However, it will be appreciated that various other means arealso possible for implementing some of the methodologies describedherein.

For example, some of these methodologies may be implemented as asoftware solution adapted to co-exist on a given web-server or back-endserver. In such embodiments, the dedicated server appliance may be ahosted system. Instead of residing behind the client's firewall and webservers, the appliance may reside in a central location (for example, atan ISP (internet service provider)). The software code used to observecustomer web sessions and to develop behavioral portraits may beinjected into the client web site using a JavaScript snippet placed inthe client's web page templates. These JavaScript snippets may beactivated in real-time to insert the most current JavaScript code fromthe host system into the web page on demand. The JavaScript may thensend the customer's click stream or other captured information back tothe portrait engine on the hosted system. Using this capturedinformation, the portrait engine generates a behavioral portrait for thecustomer, which is stored in a portrait database on the hosted system.

In a similar manner, a portrait enhanced page (PEP) may be generatedusing JavaScript code that is injected into the client web page (or atemplate from which the web page is derived) using one or moreJavaScript snippets which are inserted by the client into their web pagestructure. Following the same approach as described above, a PEPgenerator may utilize the customer portrait to generate PEPs. Thedifference in this case is that the software code for the PEP generatorresides on a hosted system instead of a dedicated server appliancebehind the client firewall.

FIG. B4 depicts a particular, non-limiting embodiment of the foregoingtype of implementation. In the system b4-0 depicted therein, a clientb4-1 is in communication with an e-commerce site b4-2 and a serviceprovider b4-3 over the Internet or another WAN b4-21. A dedicated serverappliance b4-4 is present as a hosted system at the location of theservice provider b4-3.

In operation, the client b4-1 requests a webpage from the e-commercesite b4-2. The e-commerce site b4-2 provides the requested web page,which includes JavaScript of the type previously described. TheJavaScript monitors the web activity of the client b4-1, and sendsinformation about that activity to the web servers b4-5 of the serviceprovider b4-3. The web servers b4-5 store the information in one or moredatabase servers b4-6, where it is used by the dedicated serverappliance b4-4 to build user behavioral portraits. When the client b4-1requests the next web page from the e-commerce site b4-2, the webservers b4-5 intercept the requested web page and modify it based on theuser's behavioral portrait. The web servers b4-5 then provide themodified web page to the client b4-1.

The hosted server appliance b4-4 in the particular system b4-0 depictedin FIG. B4 consists of a portrait database b4-11, a portrait engineb4-12 (which may be based on a neural network), a tool set b4-13, ananalyzer b4-14, an instrumentor b4-15, and an augmentor b4-16. Theanalyzer b4-14 analyzes information concerning the client's web activityas captured by the JavaScript inserted into the client's web pagetemplates, and works in conjunction with the portrait engine b4-12 todevelop a user portrait which is then stored in the portrait databaseb4-11. A variety of callable programs, functions, routines and the likemay be used in this analysis which are stored in the toolset b4-13. Theaugmentor b4-16 then utilizes portraits contained in the portraitdatabase b4-11 to provide modified web content. The instrumentor b4-15assembles the modified web content into a modified web page which isforwarded to the client b4-1. In order to instrument a site, the majorset of web-page templates is identified. The sections of the web page,and the existing portrait bias of each, are then categorized.Modifications to the sections are dependent on the type of portrait biasof that section.

Other variations and embodiments of the foregoing systems are alsopossible. For example, while the user portraits are preferably stored ina portrait database (see, e.g., profile database b1-43 of FIG. B1), insome embodiments of the systems and methodologies described herein,these portraits may be stored instead on a cookie defined in a clientdevice, assuming this is permitted by the user's privacy software. Insuch embodiments, the dedicated server appliance may be adapted tocollect the user information from the client device as necessary via aserver. It will be appreciated that hybrids of this embodiment are alsopossible, where some user portraits (or portions thereof) are stored inthe portrait database, and other user portraits (or portions thereof)are stored on a cookie defined in a client device. In still otherembodiments, the software for implementing the methodologies describedherein may be installed on a web server, which may query the clientdevice at appropriate intervals for updated user input information.

C. Generation of a User Behavioral Portrait

In a preferred embodiment, the software described herein applies a(possibly complex) algorithm to determine an individual's psychologicalportrait score by interpreting the individual's navigation through awebsite hosted by a server. The scoring mechanism utilizes a derivativeof a technique known as “meta-linguistic programming”. In particular,the algorithm weights user actions that can take place on a givenwebsite by category, and also by specific lexical analysis. Predicateand historical analysis is also input into the weighting. The algorithmis preferably adapted to reset to a generic weighting under appropriatecircumstances to ensure that model inaccuracies or exceptional behaviorcan be accommodated.

Preferably, the algorithm is characterized by a tipping point so that,once a sufficient portrait weighting is achieved, the software portraittriggers a number of potential actions on the website. These actions mayinclude, without limitation, re-mashing of the current website contentfor emphasis of particular data, re-routing procedures to suit a user'sbehavior in the current context, re-wording website content inaccordance with a user's presentation preferences, and re-presentationof, for example, marketing offers to focus on the user's current contextand portrait.

The methodologies described herein, and the systems and devices whichimplement them, offer a number of potential advantages. In particular,these methodologies may be used to dynamically reconfigure web pages tomake them commensurate with a user's current behavioral portrait. As aresult, the user will feel more comfortable on the website, will be lesslikely to browse away from the website, and will be more likely toreturn to the website in the future. This, in turn, will increase thelikelihood of closure on the sale of products or services advertised onthe website, will allow marketing offers to be customized for thatuser's portrait at that particular time, and will provide the user withcustomized procedures on the website.

The customer portraits which are generated may also be provided to salesteams, marketing teams, call-centers, or other such entities. Theseentities can use the information contained therein to personalizeconversations and advertising campaigns for the respective users.

Some of the methodologies described herein also provide an effectivemeans for context sensitive marketing. These methodologies provide themeans to analyze the first few interactions (e.g., mouse clicks) with awebsite and apply that information to establish a behavior portrait(frequently with respect to buying patterns) for the user for subsequentinteractions. These methodologies also provide the means to analyze thephraseology and syntax of a search, and apply that information to theuser's portrait for improved click-through. All subsequent userinteractions in the same session are then customized for the user'spreferred information sources, preferred marketing messages, preferredsales approach, and the like.

In a preferred embodiment of the methodologies described herein, ameta-linguistic process is utilized which comprehends fifteen differentpsychological attributes for a given user. These attributes aredescribed briefly in TABLE C1 below. Of course, it will be appreciatedthat various other psychological attributes (of greater or lessernumber) could be utilized in the systems, methodologies and softwaredescribed herein. It will also be appreciated that various combinationsor sub-combinations of these or other psychological attributes may beutilized in these systems, methodologies and software, or in a givenapplication or task. Moreover, the number of attributes utilized mayvary from one application to another, and from one context to another.

TABLE C1 PSYCHOLOGICAL ATTRIBUTES Psychological Attribute Description 1Decision Making Determines the way in which the customer makesdecisions, via information or reference 2 Motivation Determines therational for the website visit and completing a transaction - should theproduct offer an opportunity or prevent a problem 3 InformationPresentation Indicates how a customer prefers to receive information -process-based preferences versus behavior that demands alternatives 4Adoption Stage Indicates when, in the product life-cycle, a customer iscomfortable making a purchase - early adopter versus conventionaladoption tendency 5 Relationships Describes the level of personalinteraction desired by a customer and amount of personal relationshipfor transaction/product 6 Specificity Indicates the level of detail acustomer prefers 7 Frequency Indicates the frequency/repetition ofmessage required to close 8 Teamwork Defines level of independenceversus gradation of interaction (decision making and product use) 9Rules Determines agreed method of transaction - rules of the transaction10 Speed Determines rate of navigations/speed of clicks - indicatessurety of other weightings - can indicate level of decisiveness 11Empathy Indicates the consideration/impact of decisions on other people12 Intensity Indicates level of intensity of transaction/dialog 13Temperature Determines comfort of color preference in context oftransaction/dialog 14 Shade Determines visual stimuli and environmentpreference 15 Pattern Second level visual stimuli and environmentalpreferences

Each psychological attribute is suitably weighted by the CustomerPortrait Engine. Preferably, this weighting occurs on a numerical scalewhich is given an almost infinite number of context-sensitive andreal-time permutations for any given user at any given time. Forexample, if the Customer Portrait Engine utilizes the 15 psychologicalattributes noted above, each of these attributes may be tracked using asliding scale, with 0 as the neutral score. A weighting significantly oneither side of neutral would indicate a tendency of behavior toward thatsetting.

The manner in which user behavior may be correlated to psychologicalattributes may be better understood by considering some specificexamples. For example, a user navigating directly to productspecifications would demonstrate behavior typical of one type ofdecision making tendency, while a user browsing through customerrecommendations would demonstrate behavior typical of another type ofdecision making tendency. A customer clicking on a toothpasteadvertisement described as “bright smile” would indicate a behavioraltendency towards one type of motivation, versus a customer clicking onan offer to “prevent tooth decay”, which would demonstrate behaviortypical of another motivational tendency. A customer navigating throughcategories in a stepwise fashion (e.g., electronics, TV and video, DVDplayer) would indicate a behavioral tendency towards one type ofinformation gathering, versus a user selecting different categorieswhich would indicate a behavioral tendency towards another type ofinformation gathering.

EXAMPLE C1

The following example illustrates the application of some of themethodologies taught herein to an e-commerce transaction.

User A decides to purchase a DVD player from a multi-category website(that is, a website which sells electronics, books, clothing, andvarious other goods and services). The user's pass through the websiteinvolves the steps of:

-   -   (a) Searching for a specific DVD player model;    -   (b) Checking the detailed specifications of the model;    -   (c) Comparing the model with other top sellers;    -   (d) Adding the selected model to the shopping cart;    -   (e) Choosing expedited shipping; and    -   (f) Checkout.

Each of these individual actions during the web session can be mapped toa set of rules or a knowledge base in the Portrait Engine. An example ofsuch a mapping is shown in FIGS. C1-C2. In particular, FIG. C1 shows astandard e-commerce web page with a selection of electronics products,specifically DVD Players. FIG. C2 shows the same web page with a linkoverlay showing the mapping from each individual link to the associatedrule set within the knowledge base.

As User A performs the search in Step (a), a rule in the knowledge baseis triggered to recognize a search for a specific item with multipleterms. This type of search indicates an independent decision makingbehavior. Therefore, the score on the Decision Making attribute (seeTABLE C1 above) is increased by 10 points. Similarly, Steps (b) and (c)trigger rules for information gathering, again indicating independentdecision making behavior and increasing the score on that attribute to+30. Given the collection of Steps (a) through (d) identifying aspecific product, gathering specification data, comparing to similarmodels, and then selecting the individual DVD Player—the knowledge baserules recognize procedural behavior. Therefore, the score on theInformation Presentation attribute is increased by 20 points. In Step(e), User A displays goal-oriented behavior by expediting the shippingfor this product and the knowledge base decreases the score on theMotivation attribute by 10 points. Therefore, in this example, User A'sscores for the first three attributes in Table C1 are:

TABLE C2 User A score Psychological Attribute Score Behavior 1 DecisionMaking +30 Independent decision making 2 Motivation −10 Goal-orientedmotivation 3 Information Presentation +20 Procedural informationpresentation

By comparison, User B also goes to the same website to buy a DVD player,and this user's e-commerce dialog is as follows:

-   -   (a) Select electronics category;    -   (b) Select DVD Players;    -   (c) Choose “Top 10 Sellers”;    -   (d) Select number 1 seller;    -   (e) Check what other people said about this model;    -   (f) Check editorials and reviews;    -   (g) Select another player referenced in the reviews;    -   (h) Sort and read reviews from most negative to least negative;        and    -   (i) Checkout and purchase the referenced DVD player.

Again, with the mapping from the links and actions on the web site tothe rules in the knowledge base of the Portrait Engine, each individualaction by User B during this web session drives a score in thebehavioral portrait. In the case of User B, the focus on therecommendations and reviews of other customers, shown in Steps (c)through (h), demonstrate a collaborative decision making behavior. Therules triggered by these steps collectively decrease the score on theDecision Making attribute by 30 points. The shift in User B's productdirection, demonstrated by Step (g), triggers rules which decrease thescore on the Information Presentation attribute by 10 points, indicatinga choice-oriented preference. In Step (h), User B exhibits a desire toavoid problems, and the rules increase the score on the Motivation scaleby 20 points. In this example, User B's scores for the first threeattributes in Table C1 are as follows:

TABLE C3 User B score Psychological Attribute Score Behavior 1 DecisionMaking −30 Collaborative decision making 2 Motivation +20Problem-avoidance motivation 3 Information Presentation −10Choice-oriented preference

Both users A and B have displayed key behavioral patterns in each of thetransactions which can be used to dynamically reconfigure the web pagesdisplayed on the website. Referring again to FIGS. B1-B2, clicks andentries are passed from the clients' web browsers b1-23 through theInternet b1-27 to the enterprise web server b1-19. At this point, theweb server b1-19 passes the entry information through to the dedicatedserver appliance b1-15 (see FIGS. 1-2) via the API b1-41 softwarecomponent (in embodiments in which the methodology is implemented withsoftware installed on an application server, the web server b1-19 wouldpass the entry information to the appropriate application server b1-13instead).

The API b1-19 passes the entry and user identification informationthrough to the portrait generator b1-45, which interfaces with theportrait database b1-43 for historic user patterns and portraitinformation. At this point, a current portrait is generated for thatuser at that time. If the current portrait is determined to be strongenough for representation of the website data, the portrait and triggeris passed to the enterprise application server b1-13 responsible forpresenting the website information to the web server b1-19 for potentialre-mashing of content and offers. The result of the processing in ane-commerce application is a user-personalized web-page in the context ofthe current user behavior and buying pattern.

In some embodiments, an initial matrix of behaviors may be utilized toestablish an initial portrait for a user. Such a matrix may reflect thefact that consumers have different personalities, and exhibit differentbehaviors, based on time and topic. Such behaviors may be hard-wired,and may directly impact how a consumer feels when they are presentedwith information concerning a product or service.

EXAMPLE C2

This example illustrates how user behavior may be tracked, weighted andutilized to develop a behavioral portrait for the user.

A user's behavior on a website is recorded and analyzed. That behaviormay include such factors as:

-   -   (a) navigation selections (which navigation choices are made on        the site);    -   (b) search text parsing and analysis (search used to navigate to        a particular area on the site in conjunction with term        analysis);    -   (c) icon selection (where the user clicks);    -   (d) marketing offer selection (the type of marketing offer a        user selects);    -   (e) order of behavior (e.g., whether the user engages in        seemingly random “browsing” or more focused selections);    -   (f) speed of navigation (timed clicks, e.g., speed of checkout        or skipping less relevant pages); and    -   (g) omitted selections (what the user does not do or select).

After each action, the user's portrait is recalculated. This calculationis based upon a dynamic (real-time) weighting for each of the 15 trackedpsychological attributes shown in TABLE C1, with each attribute having aneutral setting of 0. The calculation also considers historicalknowledge of that user's previous behavior in known dialogs, and theprevious weightings in the current dialog. Each of the 15 behaviors inTABLE C1 is tracked using a sliding scale with 0 as the defaultweighting for each attribute. A weighting significantly on either sideof 0 indicates a tendency of behavior toward that setting.

As a specific example, a user may display the portrait depicted in TABLEC4 below:

TABLE C4 Example Psychological Attribute Weighting PsychologicalAttribute Weight A Decision Making 49 B Motivation 62 C InformationPresentation −53 D Adoption Stage −77 E Relationships 0 F Specificity 0G Frequency 28 H Teamwork 0 I Rules 65 J Speed 0 K Empathy 0 L Intensity0 M Temperature 0 N Shade 61 O Pattern −33Such a user is demonstrating behaviors in A, B, C, D, G, I, N and O thatare significant enough to trigger a website change.

The Portrait Engine or Portrait Generator is preferably used todynamically recalculate the customer's scores on each of the attributesduring the web session. The Portrait Engine also preferably considershistorical knowledge of that user's previous behavior in known dialogs,and the previous weightings in the current dialog. The engine may bebased on a transition table, a rule-based knowledge base, other forms ofartificial intelligence tools, or various combinations orsub-combinations of any of those tools.

While individual significant weightings (that is, weightings which mightimpact the website display, format or navigation route) may besufficient to change web page content or layout in some of theembodiments described herein, preferably, combinations of attributes,the user's behavioral history, and the context of the dialog will beused to set the “trigger” flag and to intercept and analyze the web pagebeing displayed to the user. Typically, the page to be displayed will bemodified (that is, a Portrait Enhanced Page (PEP) will be generated). Aspreviously stated, triggers and fail-safes may be implemented throughoutthe system to ensure that PEPs are not displayed until the behavior isknown with a degree of certainty, or, for example, if the userdemonstrates behavioral swings while in a transaction.

D. Modification of Web Site Content Based on User Behavioral Portrait

It will be appreciated that Portrait Enhanced Pages (PEPs) may beproduced in the methodologies taught herein by amending web pages in anumber of ways. Such changes include, but are not limited to:

-   -   (a) content reorganization/re-prioritization;    -   (b) content addition;    -   (c) content deletion;    -   (d) style alterations of content (i.e. highlighting, changing        font/size/color, borders, padding, etc);    -   (e) content folding (placing content in collapsible/expandable        containers);    -   (f) marketing offer personalization;    -   (g) icon personalization;    -   (h) language personalization;    -   (i) expedited navigation paths through the website;    -   (j) elongated (added pages) navigation paths through the        website.

As previously noted, in some embodiments, the methodologies describedherein may be utilized to modify or augment web page or website contentor presentation as a function of user behavioral portrait. This processmay be appreciated with respect to FIG. D2. As seen therein, thebehavior of a user on a website is captured d2-1, and is analyzed tobuild a behavioral portrait d2-2 for that user. It is then determinedwhether content modification is appropriate in light of the user'sbehavioral portrait d2-3. If so, the content on the website is modified,augmented or enhanced d2-4 in light of the user's behavioral portrait.Typically, as seen with reference to the sample web pages d2-5 (shown ingreater detail in FIG. D3), this will result in a portrait enhanced webpage which is substantially different from the standard, unmodified webpage.

FIG. D1 provides an overview of some possible types of webpagemodification that may be effected with some of the methodologiesdescribed herein. As seen therein, these modifications preferably occurdynamically and in real-time, and may involve varying levels ofcomplexity and interaction with the user.

In the particular embodiment depicted in FIG. D1, the first level d1-2of web page augmentation involves emphasizing or de-emphasizing certaincontent. This may result in content being highlighted or being presentedwith an increased or decreased font size. It may also result in thecollapse or expansion of certain menus, and in the repositioning ofcontent within a given web page.

Again referring to the particular embodiment depicted in FIG. D1, thesecond level d1-3 of webpage augmentation involves personalizing offersappearing on the web page. This may involve, for example, presentingbanner ads, pop-ups, or hot links whose content is targeted to the userbased on their behavioral portrait. This may also involve repositioningof offers on the web page in light of the user's behavioral portrait.

Still referring to the particular embodiment depicted in FIG. D1, thethird level d1-4 of webpage augmentation involves personalizing thecontent appearing on the web page. This may involve modifying the formatin which content appears (e.g., tab reviews), modifying data appearingon the web page, mash-up of local content (e.g., reviews on the homepage), and mash-up of remote content.

Still referring to the particular embodiment depicted in FIG. D1, thefourth level D1-5 of webpage augmentation involves the re-routing,expedition or delay of certain webpage content. For example, thecheck-out process required to purchase an item advertised on the webpage may be modified in light of the user's behavioral portrait toensure a higher level of satisfaction and success. Up-sell processesappearing on the web page (that is, the practice of suggesting higherpriced products or services to a customer who is considering a purchase,such as an offer for a better version of the same product or service theconsumer is considering purchasing) may also be modified.

The augmentor (see FIG. B4) uses portrait attributes and/or type fordeciding what applies to a given web page and a specific portrait orportrait type. In a preferred embodiment, the augmentor uses JavaScriptinjection, which requires the client to include a JavaScript snippet oneach page that should be tailored to the current website user. Multipleinsertion points may be required, depending on the type of desiredmodifications and/or page complexity.

Modification of the webpages is preferably accomplished via JavaScriptthat gets executed in the customer□s browser. The JavaScriptmanipulating the page is preferably structured as rules along with acore library (jQuery+extensions). Only the rules specific for thecombination of the client's web-page and the customer□s portrait type orattributes will be downloaded into the browser.

The jQuery JavaScript library is preferably utilized for creating rulesto identify page sections (portrait-based content) and to manipulatecontent. Extensions to the jQuery library may be created to simplifyrule creation, data selection, and content manipulation. In order toreduce bloat, it is preferred to use two types of extensions: thosecommon across clients, and those specific to a client. In order tofurther minimize load and processing time, the jQuery library may bereduced to the minimum code set required for the rules. The resultinglibrary may then be compressed.

In order to instrument a site, the major set of web-page templates isidentified. The sections of the web page, and the existing portrait biasof each, are then categorized. Modifications to the sections aredependent on the type of portrait bias of that section. The major typesof typical content are listed in TABLE D1 below.

TABLE D1 Content Types Decision Motivation Presentation Best SellersOther Opportune Featured Items Other Choice Popular Other Search SelfProcess Recommendations Other User Reviews Other Expert ReviewsSelf/Other Promotions Opportune Advertisements Other Product DetailsSelf Related Items Other Choice Accessories Support Other SafetyInformational Self Crumbs Process Footnotes Process Terms/PoliciesSafetyThe foregoing types of content may be represented in the followingformats:

-   -   Headings    -   Lists/Tables    -   Links    -   Text    -   Images    -   Form Items    -   Groupings of the above items.

D1. Level 1 Modifications

Level 1 modifications may include bolding, highlighting, changing thesize of text, collapsing sections/lists, repositioning sections,expanding sections/lists, and deleting sections/content using rulesbased on portrait attributes. Modifications of the identified sectionson given web pages depend on the current user's portrait data and on theexisting content type in the given sections. For example, reviews andrecommended product lists can be collapsed for a ‘self’ user. Likewise,reviews and recommended product lists might be positioned moreprominently on the page for an ‘other’ user. Examples of some possiblemodifications which may be implemented for various users are set forthin TABLES D2-D4 below.

TABLE D2 Modifications of Sections Based on User Behavioral PortraitType: Self/Other Self Other Best Sellers Collapse Move to higherposition Move to lower positioning Increase font size and Reduce fontsize and bolding of header bolding of heading and text Featured ItemsCollapse Move to higher position Move to lower positioning Increase fontsize and Reduce font size and bolding of header bolding of heading andtext Popular Collapse Move to higher position Move to lower positioningIncrease font size and Reduce font size and bolding of header bolding ofheading and text Search Move to higher position No manipulationnecessary Recommendations Collapse Move to higher position Move to lowerpositioning Increase font size and Reduce font size and bolding boldingof heading of header and text User Reviews Collapse Move to higherposition Move to lower positioning Increase font size and Reduce fontsize and bolding of header bolding of heading and text Expert Reviews Nomanipulation necessary No manipulation necessary Advertisements RemoveNo manipulation necessary Product Details Move to higher positionCollapse Increase font size and Move to lower positioning bolding ofheading Reduce font size and bolding of header and text Related ItemsCollapse Move to higher position Move to lower positioning Increase fontsize and Reduce font size and bolding bolding of heading of header andtext Accessories No manipulation necessary Move to higher positionIncrease font size and bolding of heading Support Collapse Move tohigher position Move to lower positioning Increase font size and Reducefont size and bolding bolding of heading of header and textInformational Move to higher position Collapse Increase font size andMove to lower positioning bolding of heading Reduce font size andbolding of header and text

TABLE D3 Modifications of Sections Based on User Behavioral PortraitType: Opportune/Safety Opportune Safety Best Sellers Move to higherposition No manipulation necessary Increase font size and bolding ofheading Promotions Move to higher position Collapse Increase font sizeand bolding Move to lower positioning of heading Reduce font size andbolding of header and text Support No manipulation necessary Move tohigher position Increase font size and bolding of heading Terms/ Nomanipulation necessary Move to higher position Policies Increase fontsize and bolding of heading

TABLE D4 Modifications of Sections Based on User Behavioral PortraitType: Choice/Process Opportune Safety Featured Move to higher positionNo manipulation necessary Items Increase font size and bolding ofheading Search No manipulation necessary No manipulation necessaryRelated Move to higher position No manipulation necessary Items Increasefont size and bolding of heading Crumbs No manipulation necessary Moveto higher position Increase font size and bolding of heading FootnotesCollapse No manipulation necessary Move to lower positioning Reduce fontsize and bolding of header and text

D2. Level 2 Modifications

Level 2 modifications preferably target specific portrait types byaltering images and/or text. There are at least two options formodifying text or images on a web page. One is to create an explicitaugmentor rule, and the other is to define images for a page and sectionin the portal.

The portal will preferably include web pages where the client canspecify images and text that will be dynamically displayed, based on theportrait type of the user. Prior setup work will typically includedefining the page templates (URL regex matching) and page sections(jQuery selectors) using friendly names. For example, the product pagemight have a friendly name of ‘Product Page’ and the advertising sectionmight have a friendly name of ‘Top Right Advertisement’. The user wouldthen be able to specify alternate images/text based on the portraittype. Images may be specified using only a URL. Text content willtypically be specified in HTML format.

D3. Level 3 Modifications

Level 3 modifications preferably add or replace content from the client,and add mash-up content from third party web sites. The portal willinclude web pages where the client can specify an RSS feed or URL fromwhich to retrieve the mash-up content, along with a list of the desiredfields from the resulting content. The mash-ups may be handled by theclient□s development team to provide for optimal performance and clientcontrol.

Client content may be retrieved through a number of methods. These mayinclude, without limitation:

-   -   an AJAX request from the web browser to the client□s systems,        which is then displayed;    -   instrumentation of links with a portrait type indicator to allow        the client to display appropriate data on subsequent pages;    -   client addition of multiple content options (one for each        portrait type), which the JavaScript will appropriately display        based on portrait type; and    -   client data retrieval through the use of a client provided API        (this approach is not preferred, as it may require very tight        integration and substantial custom code).

D4. Level 4 and 5 Modifications

Level 4 modifications preferably provide re-routing, expediting, anddelaying website navigation.

Level 5 modifications preferably involve product descriptionpersonalization.

In the foregoing process, it is preferred that the clients identify webpage sections by ids and classes, as this may result in higherperformance and reduced rule volatility. It is also preferred that theclient assume as much responsibility for dynamic content as possible, asthis may provide for higher performance, greater stability andconsistency.

EXAMPLE D-1

This example illustrates the modification of content (on the website ofan on-line computer retailer) for two different types of users inaccordance with a particular, non-limiting embodiment of themethodologies described herein.

With respect to FIG. D4 and FIG. D5, some of the behavioral traits oftwo different hypothetical users are summarized. With respect to FIG.D4, the first user (“Trish”) is a “thinker” type user. Trish is a typeof user who is in control of her decisions d4-1, and will often say “Ihave a gut feel” d4-2. Trish typically makes her decisions based on areview of detailed specifications and trusted sources d4-3, and ismotivated by how an offered product will help her to achieve her goalsd4-4. She wants to see a positive result from her purchase d4-5, isheavily dependent on process and procedure d4-6, and is not likely tomove to the next step in a purchase process before the current step iscompleted d4-7.

By contrast, the second hypothetical user (“Karl”—see FIG. D5) is a“browser” type user. Karl likes to gather opinions on his potentialpurchase d5-1. It is important to Karl that the product is positivelyreviewed, has won awards, and is a top 10 choice d5-2. Karl is motivatedmore by avoiding problems then by missing opportunities d5-3. Karl willoften ask “What do you think?” d5-4. He feels constricted by process,and wants to be able to choose from many options d5-5. As a consumer,Karl requires multiple paths to the same result d5-6.

The behavioral portraits of Trish and Karl are summarized in FIGS. D6and D7, respectively. As seen therein, their behavioral portraits inthis particular embodiment consist of a scaled weighting of 15 differentbehavioral characteristics, including decision, motivation, information,adoption, relationships, specificity, frequency, teamwork, rules, speed,empathy, intensity, temperature, shade and pattern.

Referring now to FIGS. D8-D13, the manner in which particular web pageson a web site are modified in light of Trish's behavioral portrait isillustrated.

With reference to FIG. D8, on the homepage d8-4 of the web site, theadvertisement appearing on the top of the home page is personalized d8-1so that the language in the advertisement is more aligned with Trish'sdecision-making attribute. Moreover, the search bar is emphasized d8-2.Given Trish's behavioral portrait, it is likely that Trish knows whatshe's here for, and she prefers web sites that allow her to get to aresult quickly. This behavioral trait is accommodated by emphasizing thesearch bar, which allows her to navigate through the site more quickly.

With reference to FIG. D9, on the notebook category page d9-7 of the website, the fact has been recognized that the standard notebook categorypage does not give someone of Trish's behavioral portrait sufficientdetailed information to make an informed choice. Consequently, the pagehas been augmented by mashing up additional specification informationd9-3 from other areas on the website. In addition, non-relevant contenthas been collapsed and de-emphasized d9-5, thus resulting in a web pagethat more accurately reflects Trish's preferences. As with the home paged8-4 (see FIG. D8), advertising has been personalized d9-1 and offershave been reformatted d9-4 in accordance with Trish's behavioralportrait. Also, the search bar has once again been emphasized d9-2 toallow Trish to get to a result quickly.

With reference to FIG. D10, the notebook model page d10-6 of the website has been personalized with the addition of specification detaild10-2 and re-formatted offers d10-3. Non-relevant content has beencollapsed d10-7, and search tools/bread crumbs have been emphasizedd10-1.

With reference to FIG. D11, the “1520” page d11-6 of the web site hasbeen augmented so that the detailed technical specifications of theproduct under consideration are opened by default d11-4. The advertisingappearing on the web page, and the layout of the web page itself,continues to be customized d1-1 in accordance with Trish's informationpresentation preferences. The reviews which would ordinarily appear onthis web page have been collapsed into a reviews tab d11-3 so that Trishmay choose that content if she decides to, but is otherwise notpresented with it. Once again, search tools and bread crumbs have beenemphasized d11-2.

Referring now to FIG. D12, the featured systems page d12-5 of the website has been customized in light of Trish's preferences with respect toadvertising and page layout d12-1. A reviews tab has been added d12-3 tothe page so that the information it contains is de-emphasized, but isavailable to Trish if she chooses it. Search tools and bread crumbs haveagain been emphasized d12-2.

With reference to FIG. D13, the configuration page d13-4 of the web sitehas been reconfigured so that Trish is redirected past the services paged13-1. This reflects her suspicion of content which is introduced intothe close process without her consent. All other close pages which areappropriate for Trish's buying patterns are available. Non-relevantcontent as been collapsed d13-2.

Referring now to FIGS. D14-D19, the manner in which particular web pageson the same web site are modified in light of Karl's behavioral portraitis illustrated.

With reference to FIG. D14, the homepage d14-4 of the web site has beenmodified to reflect Karl's preference for gathering product informationfrom reviews, testimonials and customer success stories. To that end,review content d14-3 has been added to the page, and a community linkd14-2 has also been provided. Moreover, since Karl is motivated bychoices, the advertising content appearing on the page has beenpersonalized D14-1 to stress the choices that the product offers.

With reference to FIG. D15, which shows the notebook selection paged15-6 of the web site, Karl's online buying patterns have indicated aneed for choices and recommendations. He is less concerned with detailedspecifications, but prefers peer reviews and popular choices. Therefore,some of the product specification information on this page has beenreplaced with a listing of all notebooks available in all product linesd15-2, and the user ratings of each of these products is specified. Inaddition, recommended links have been expanded d15-3, and theavailability of help and support has been emphasized d15-4. Advertisinghas also been personalized in light of Karl's behavioral portrait d15-1.

Referring now to FIG. D16, which shows the featured system page d16-6,this page of the web site has been modified to reflect the fact thatKarl's primary information source is peer reviews. Consequently, thespecification content has been collapsed d16-3 (though it is stillaccessible), and a reviews tab has been added d16-2 and expanded d16-4.The advertising appearing on the web page has again been personalizedd16-1 to reflect Karl's behavioral portrait.

Karl's check-out page d17-3 is depicted in FIG. D17. Karl is opened torecommended configuration options for his purchase, including serviceoptions. Therefore, this page is not skipped as it was for Trish. Again,the advertising appearing on this web page has been personalized d17-1to reflect Karl's behavioral portrait.

FIG. D18 depicts Karl's configuration page d18-5. Karl displays buyingbehaviors that indicate a suspicion of process and of being “led down apath”. He prefers to be able to somewhat randomly access sections of thecheck-out process, and he always desires choices. Consequently, theconfiguration page d18-5 has been modified to ensure that Karl iscomfortable with the check-out process. In particular, new tab formatsd18-1, new navigation buttons d18-2, and new instructions d18-3 areprovided to allow him to randomly access sections of the check-outprocess.

FIG. 19 depicts Karl's check-out page d19-3. Since Karl is open topurchasing additional products if they are recommended by experts orpeers, the “essential add-ons” tab has been moved d19-1 to the primaryposition in the configuration section.

E. Web Site Analysis Based on User Behavioral Portrait

As previously noted, some of the methodologies described herein may beutilized to provide an analysis of browsing behavior on a web site as afunction of behavioral portrait type. Such an analysis may help theowner of a website to improve e-commerce closure rates, to improve therate of return visits to the website, to understand why customers (orpotential customers) leave the website, to understand how to improve theeffectiveness of web marketing programs and offline programs, to betteralign offline marketing with website content, and to understand how tobest communicate with different customer segments.

EXAMPLE E1

This example illustrates the types of analytics (see FIG. A1) which maybe generated on an e-commerce web site using some of the methodologiesdescribed herein.

FIGS. E5-E12 represent the results of an analysis report of the typedescribed herein which was prepared on an e-commerce web site. Such ananalysis provides trends and indicators which are derived fromportrait-based analytics for the website over a predetermined period oftime.

FIGS. E5-E6 depict the number of visits to the web site over apredetermined period of time, broken down in percentages by consumerbehavioral portrait type (FIG. E5) and by the total number of visits byeach behavioral portrait type (FIG. E6). These results demonstrate that,with respect to the particular web site under consideration, consumershaving portrait types F and H showed a visit rate to the web site whichwas much higher than the average visit rate, while consumers havingportrait types B, D, and G showed a much lower than average visit rateto the website. These results suggest that the language used in externalmarketing campaigns (both online and offline) is attracting consumers ofportrait types F and H to the website significantly more than consumershaving other portrait types. Similarly, these results suggest that thelanguage used in the external marketing campaigns is attractingconsumers with B, D and G type behavioral portraits significantly lessthan consumers with other behavioral portrait types. This result may beintentional, or it may be an indicator that traffic generationactivities are linguistically skewed to F and H behavioral portraittypes, and away from B, D, and G behavioral portrait types.

FIGS. E7-E8 depict the number of return visits to the website over apredetermined period of time, broken down in percentages by userbehavioral portrait type (FIG. E7) and by the total number of visits byeach behavioral portrait type (FIG. E8). As these results illustrate,consumers having behavioral portrait types F and H are showing muchgreater than average return rates as compared to other behavioralportrait types. The combination of above average total visit numbersdescribed above, and the very high return rates shown here, indicatesthat there is good alignment between traffic generation activities andwebsite behavior.

As these results also illustrate, behavioral portrait types B, D, and Gshow a much lower than average return rate as compared to otherbehavioral portrait types. The combination of below average total visitnumbers for these behavioral portrait types as indicated above, combinedwith the very low return rates indicated here, suggest an issue withwebsite behavior not matching expectations for consumers of thisbehavioral portrait type.

FIGS. E9-E10 summarize the close rates as a function of behavioralportrait type. These data suggest that consumers of behavioral portraittype H are showing below average close rates when compared otherbehavioral portrait types. This behavior indicates a potential issuewith the check-out process for consumers of this behavioral portraittype. The high visitor rates indicate good traffic generation. Highreturn rates imply that the website informational structure is sound.However, the low closure rate is a concern. This may be due to amisalignment of the closure process language and behavior with the restof the web site. Thus, it would be prudent to check the top rankingabandoned pages for consumers of this behavioral portrait type todetermine the key pages to be examined and tuned.

Consumers of behavioral portrait types C, E, and G are showing very lowclose rates on the website when compared to other behavioral portraittypes. The very poor visit rates exhibited by consumers of thisbehavioral portrait type as compared to consumers of other behavioralportrait types strongly suggests that traffic generation for consumersof these behavioral portrait types is not working. When it does work,these visitors close less than those with other portrait types. Thisresult may be intentional. However, if it is not, these results suggestthat the abandoned pages should be checked, and the language orpresentation on these pages should possibly be modified, to better alignthem with consumers of these behavioral portrait types.

Consumers of behavioral portrait types A and F are closing andabandoning at approximately the same rates. These indicators demonstratevery good alignment between marketing campaigns and website behavior.Nonetheless, the web site manager may wish to check the abandoned pagesand program/marketing campaign statistics to determine whether furthertuning can occur.

Consumers of behavioral portrait types B and D are showing very highclose rates when compared to consumers of other behavioral portraittypes. This result suggests a serious misalignment of traffic generationactivities (online and offline marketing campaigns, pay-per-click, etc.)and the behavior and language used on the website. Consumers havingthese portrait types are closing at a high rate, but traffic generationprograms in the outbound marketing activities in use appear to bedriving these customers to the site in lower numbers as compared toconsumers having other behavioral portrait types. Alignment of thelanguage used in those marketing campaigns may drive higher levels ofqualified, closing traffic to the site. The programs and advertisingstatistics may also be checked for further details on what is workingand what is not working.

FIG. E11 summarizes the average time spent on the web site as a functionof consumer behavioral portrait type. The results shown therein indicatethat consumers of behavioral portrait types G and H are spending anaverage amount of time on the website. These consumers are leaving thewebsite early in the website page hierarchy, but are spending an averagetime on the pages they are viewing. This may indicate that pages on thewebsite are lengthy and informational, but are not targeted to consumersof these behavioral portrait types. Another possibility is that thewebsite behavior does not offer a clear path to the next step or aprocedure for closing on the product. This would suggest that theabandoned pages and portrait definitions should be checked for furtherinformation.

FIG. E12 summarizes the link analysis for the website as a function ofconsumer behavioral portrait type. The results shown therein suggestthat consumers of behavioral portrait types C and E are clicking throughan average number of pages on the website, and yet are abandoning thewebsite more often than not. This may be an indication that the languageused in marketing and outbound programs is misaligned with websitebehavior. In this case, it is possible that the website demonstrates asimilar flow throughout and that consumers of this behavioral portraittype abandoned the website at the first page that did not give them theinformation that they needed. These results suggest that the abandonedpages should be checked for more details on where these consumers areleaving. It is also possible that consumers are satisfied with the firstfew interactions with the web site, and then hit an area ofmisalignment. Again, the abandoned page details may be consulted toprovide more information on this issue.

These results also show that consumers of behavioral portrait types Band D are clicking through an average number of pages on the website.Consumers having these behavioral portrait types are closing within afew clicks. This suggests that the website is organized appropriatelyfor consumers of these portrait types, and that such consumers arefinding the information that they need.

The results also demonstrate that consumers of behavioral portrait typesG and H are clicking through very few pages on the website and areabandoning the website very quickly (on average, within the first fewclicks). It is possible that the first few pages presented to thesecustomers are the problem. For example, specifically ending pages fromoutbound programs or common entry points from pay-per-click activitiesmay be demonstrating the suspect behavior. These results suggestchecking the abandoned page details, and focusing on tuning these pagesfirst. Another possible approach would be to test specific marketingprograms with new landing pages more specifically focused on consumersof these portrait types.

In a preferred embodiment of the web site analytical software andmethodologies provided herein, a knowledge base is provided whichconducts real-time analysis of the behavioral data collected from websessions. Using a set of rules or other analytical tools, the exhaustiveset of data may be mined for patterns, trends, alignments, disconnects,and other observations. This analysis recognizes and identifies thecomplex conditions involving various combinations of the data andstatistics collected by the Portrait Engine (the software module thatgenerates user behavioral portraits; see FIG. B4). The key results canbe highlighted, and a manageable set of recommendations can be madebased on those results. In this way, a marketing team utilizing thereport receives more actionable information, instead of an overload ofdata with no ability or means of identifying the critical content.

The generated report describes the web analytics and behavioral datapresented in the report's graphs by pointing out the importantobservations marketing or e-commerce teams should notice. In the exampleshown above, the rules engine identifies that customers with Portraittype F have a much higher visit rate than average. However, the closurerate for Portrait type F is considerably lower than that of Portraittype B. In this particular example, the results could indicate that theoutbound marketing campaign is more oriented to those customers whodisplay the behaviors of Portrait F, while the site itself is morecomfortable to customers with behaviors of Portrait type B.

F. Strategic Information for Sales Groups based on User BehavioralPortrait

The user behavioral portraits obtainable with the systems andmethodologies described herein may be utilized by various groups toenable them to communicate more efficiently with the user to whom thebehavioral portrait corresponds. Such groups include, withoutlimitation, telemarketers, call centers, sales representatives, helpcenters, and sales teams making face-to-face sales pitches. Theinformation contained in the user behavioral portrait may be utilized,by itself or in combination with general analyses or recommendations forusers of the general behavioral portrait type, to understand how tocommunicate with the user more effectively, to understand how to handlecommon objections the user may have, and to identify the best manner inwhich to close a sale with the user.

In the case of call centers, sales forces, or other similar teams, theportrait of the customer (or potential customer) may be used to generatea customer passport. A review of the customer passport, a non-limitingexample of which is depicted in FIG. F1, may provide the employee withinformation about the customer's preferred approach for makingdecisions, their basic motivation for completing a transaction, theirpreferred means of receiving information, and their behavior in terms ofeach of the remaining attributes. Recommended language may be providedto the employee to create the highest level of comfort for the customer.Similarly, language which will create a dissonance for the customer maybe identified. Furthermore, specific language recommendations may beprovided to advise the employee on preferred or optimal methods forclosing or re-engaging the customer, or responding to common objectionsthe customer may have. Finally, a sample engagement script may beprovided for the employee's specific company and situation.

Again, all of this information and these suggestions may be based on thecustomer's portrait which is dynamically generated during their websession. It will thus be appreciated that the passport allows a personusing it to understand the proper language to use to describe productsand services to the customer, to understand the proper way topersonalize and position marketing messages for the customer, and tounderstand the proper way to close each customer.

The foregoing methodology is depicted schematically in FIG. F2. As seentherein, after a user's behavior on a web site has been captured f2-1, acomprehensive customer portrait is built f2-2 based upon that behavior.This portrait is then used to generate a customer passport f2-3 whichmay be utilized by any of the groups described above to facilitateinteraction with the customer f2-4.

G. Other Concepts and Applications

G1. General Internet Applications

While much of the foregoing discussion has dealt with web-basedmarketing, it will also be appreciated that the systems, methodologiesand software disclosed herein are not particularly limited to thatapplication. In particular, the systems, methodologies and softwaredisclosed herein may be applied to a wide variety of applications wherecustomized web pages and/or content are desirable, including, forexample, gaming, nonprofit websites, and general Internet use.

As a particular, non-limiting example, methodologies of the typedescribed herein may be utilized to provide customized web pages or webcontent in noncommercial applications such as government web sites. Forinstance, these methodologies may be utilized to provide customizationof an IRS web site. Since many taxpayers consult such a site when theyhave questions relating to their taxes, and since some of themethodologies described herein may be utilized to ensure thatinformation is being presented to these users in a way that is mosthelpful to them, the use of these methodologies may ensure that usersare more likely to stay on the website longer and to find theinformation they are looking for. This, in turn, may reduce the burdenon the IRS call center, and may also reduce the number of mistakes insubsequent tax returns.

In other embodiments, behavioral portraits for users may be derived fromweb sites which are unrelated to a business. Those behavioral portraitsmay then be utilized in the conduct of the business with respect to theindividual or to the public and general.

For example, the behavior of a particular individual on a noncommercialwebsite may be monitored, and a behavioral portrait of the individualmay be derived. That behavioral portrait may then be applied tocustomize content on a commercial website, or may be provided to callcenters or other marketing groups to help those groups understand how tointeract with the individual. Such embodiments make it conducive for abusiness to underwrite a website which provides free content of interestto the general public, since the business may then apply the informationit has learned on the nonprofit site to provide a commercial benefit(albeit indirectly) to its commercial business.

G2. Business Models

The methodologies disclosed herein may also give rise to a number ofunique business models. In the current business climate, businesses areincreasingly looking for methods to ensure that the products andservices they purchase are not burdened with onerous up-front costs.Rather, the current trend is for products and services which demonstratebusiness value before payment. Consequently, a number of “pay-per-use”and “pay-per-performance” products are available today.

In the present case, some of the systems and methodologies describedherein lend themselves well to a cost model that allows the customer todecide whether the product has impact as the transaction occurs. Theunit of cost may be taken as the Portrait Enhanced Page (i.e., themodified web page for a particular customer during a particular websession). As the psychological behavior of the customer is tracked andweighted, the software described herein may be used to generate new webpages for that dialog. These Portrait Enhanced Pages (PEPs) may beoffered to the website provider, who may either display the PEPs or optout of doing so. If the PEP is displayed, the website provider may becharged a small fee, thus eliminating heavy up-front licensing costs andallowing the customer to determine the limit of the investment basedupon business value. This model also lends itself to subscriptionservices, where an Internet service provider may package availability ofPEPs with their higher end service packages.

In other business models possible in accordance with the teachingsherein, the user portraits described above may be provided to variousthird party entities, such as call centers, marketing teams, salesforces, and the like. Preferably, the fee for such portraits is basedonly on the number of portraits provided to, and accepted by, the thirdparty entity, although various other payment options may be utilizedinstead. For example, the party providing the portraits may earn acommission which is calculated as a percentage of, or is otherwise basedon, the value of one or more successful sales which are subsequentlymade by the third party entity to the user whose portrait is provided tothat entity.

In some embodiments of the methodologies described herein, the provisionof user portraits may be coupled with the provision of other leadsrelating to the users whose portraits are provided. The party providingthe user portraits may be the same as, or different from, the partyproviding the leads. For example, the party providing the user portraitsmay be a search engine, and the party providing the leads may be thesponsor of one or more websites that the user has visited, or a businessentity (such as, for example, a travel agency, retailer, bank or creditcard company) that the user has done business with in the past. In suchembodiments, the third party entity may decide whether or not to acceptthe portrait with the lead, and fees may be charged only when portraitsare exchanged.

The resulting fees may be apportioned among the search engine and leadgeneration company in accordance with a contractual agreement betweenthe organizations. If the party providing the user portraits is the sameas the party providing the leads, a premium may be charged for theprovision of a lead in conjunction with a portrait, as compared to thefee charged for providing only a portrait.

The above description of the present invention is illustrative, and isnot intended to be limiting. It will thus be appreciated that variousadditions, substitutions and modifications may be made to the abovedescribed embodiments without departing from the scope of the presentinvention. Accordingly, the scope of the present invention should beconstrued in reference to the appended claims.

1. A method for analyzing a website, comprising: recording data relatingto the online behavior of a plurality of users through the website;building a behavioral portrait for each of the plurality of users basedon the data; categorizing the behavioral portraits of the plurality ofusers into a plurality of portrait types; and analyzing the behavior ofthe plurality of users on the website as a function of portrait type. 2.The method of claim 1, wherein the recorded data relates to behaviorselected from the group consisting of (a) items clicked on by a user;(b) the number of times a user abandons the web site; (c) the number ofreturn visits a user makes to the website; (d) the number of onlinepurchases made by a user on the website; (e) the average time a userspends on the web site; and (f) the number of pages visited on the website by a user.
 3. The method of claim 2, wherein the recorded data ismeasured over a fixed period of time.
 4. The method of claim 1, whereinanalyzing the behavior of the plurality of users on the website as afunction of portrait type involves determining the relative frequencywith which items are clicked on by a user as a function of portraittype.
 5. The method of claim 1, wherein analyzing the behavior of theplurality of users on the website as a function of portrait typeinvolves determining the relative frequency with which users abandon thewebsite a function of portrait type.
 6. The method of claim 1, whereinanalyzing the behavior of the plurality of users on the website as afunction of portrait type involves determining the relative frequencywith which users make return visits to the website a function ofportrait type.
 7. The method of claim 1, wherein analyzing the behaviorof the plurality of users on the website as a function of portrait typeinvolves determining the relative frequency of online purchases on thewebsite a function of portrait type.
 8. The method of claim 1, whereinanalyzing the behavior of the plurality of users on the website as afunction of portrait type involves determining the average time usersspend on the website a function of portrait type.
 9. The method of claim1, wherein analyzing the behavior of the plurality of users on thewebsite as a function of portrait type involves determining the averagenumber of pages users visit on the website a function of portrait type.10. The method of claim 2, further comprising: outputting graphicalinformation representing the recorded data as a function of portraittype.
 11. A method for analyzing a web page, comprising: categorizingthe features appearing on the web page in terms of at least onebehavioral trait which selection of the feature would indicate; andcreating a graphical overlay which reflects the categorization of thefeatures.
 12. The method of claim 11, wherein the graphical overlaycomprises a color-coded map.
 13. The method of claim 12, furthercomprising: superimposing the color-coded map over the web page.
 14. Themethod of claim 11, further comprising: using the categorization offeatures to create behavioral portraits of users accessing the web page.